Systems and Methods to Track Vehicles Proximate Perceived By an Autonomous Vehicle

ABSTRACT

The present disclosure provides systems and methods for tracking vehicles or other objects that are perceived by an autonomous vehicle. A vehicle filter can employ a motion model that models the location of the tracked vehicle using a vehicle bounding shape and an observation model that generates an observation bounding shape from sensor observations. A dominant vertex or side from each respective bounding shape can be identified and used to update or otherwise correct one or more predicted shape locations associated with the vehicle bounding shape based on a shape location associated with the observation bounding shape.

FIELD

The present disclosure relates generally to tracking vehicles or otherobjects. More particularly, the present disclosure relates to trackingvehicles that are perceived by an autonomous vehicle by using a vehiclefilter that employs prediction and observation bounding shapes.

BACKGROUND

An autonomous vehicle is a vehicle that is capable of sensing itsenvironment and navigating without human input. In particular, anautonomous vehicle can observe its surrounding environment using avariety of sensors and can attempt to comprehend the environment byperforming various processing techniques on data collected by thesensors. Given knowledge of its surrounding environment, the autonomousvehicle can identify an appropriate motion path through such surroundingenvironment.

Thus, a key objective associated with an autonomous vehicle is theability to perceive the location of objects (e.g., vehicles) that areperceived by the autonomous vehicle and, further, to track the locationsand motions of such objects over time. The ability to accurately andprecisely detect and track the locations of vehicles and other objectsis fundamental to enabling the autonomous vehicle to generate anappropriate motion plan through its surrounding environment.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or can be learned fromthe description, or can be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to acomputer-implemented method for tracking vehicles that are perceived byan autonomous vehicle. The method includes obtaining, by one or morecomputing devices included in the autonomous vehicle, state datadescriptive of a previous state of a tracked vehicle that is perceivedby the autonomous vehicle. The previous state of the tracked vehicleincludes at least one previous location of the tracked vehicle or avehicle bounding shape associated with the tracked vehicle. The methodincludes predicting, by the one or more computing devices, one or morefirst locations for a first vertex or a first side of the vehiclebounding shape based at least in part on the state data. The methodincludes obtaining, by the one or more computing devices, observationdata descriptive of one or more sensor observations. The method includesdetermining, by the one or more computing devices, a second location ofa second vertex or a second side of an observation bounding shapeassociated with the tracked vehicle based at least in part on theobservation data. The method includes determining, by the one or morecomputing devices, at least one estimated current location of the firstvertex or the first side of the vehicle bounding shape associated withthe tracked vehicle based at least in part on a comparison of the one ormore first locations for the first vertex or the first side of thevehicle bounding shape to the second location of the second vertex orthe second side of the observation bounding shape.

Another example aspect of the present disclosure is directed to acomputer system. The computer system includes one or more processors andone or more non-transitory computer-readable media that collectivelystore instructions that, when executed by the one or more processors,cause the computer system to perform operations. The operations includepredicting, by the one or more computing devices, one or more firstshape locations for a vehicle bounding shape associated with a trackedvehicle that is perceived by an autonomous vehicle. The operationsinclude identifying, by the one or more computing devices, a firstdominant vertex for each of the one or more first shape locationspredicted for the vehicle bounding shape. The operations includeobtaining, by the one or more computing devices, observation datadescriptive of one or more sensor observations. The operations includedetermining, by the one or more computing devices, a second shapelocation for an observation bounding shape associated with the trackedvehicle based at least in part on the observation data. The operationsinclude identifying, by the one or more computing devices, a seconddominant vertex for the observation bounding shape. The operationsinclude determining, by the one or more computing devices, at least oneestimated shape location for the vehicle bounding shape based at leastin part on a comparison of a first location of the first dominant vertexfor each of the one or more first shape locations to a second locationof the second dominant vertex for the observation bounding shape.

Another example aspect of the present disclosure is directed to anautonomous vehicle. The autonomous vehicle includes a tracking systemthat is operable to track vehicles perceived by the autonomous vehicle.The tracking system includes a motion model operable to predict one ormore first shape locations for a vehicle bounding shape associated witha tracked vehicle. The tracking system includes an observation modeloperable to provide a second shape location of an observation boundingshape associated with the tracked vehicle based at least in part onobservation data derived from one or more sensors. The autonomousvehicle includes one or more processors and one or more non-transitorycomputer-readable media that collectively store instructions that, whenexecuted by the one or more processors, cause the one or more processorsto perform operations. The operations include identifying a firstdominant vertex or a first dominant side for each of the one or morefirst shape locations predicted for the vehicle bounding shape. Theoperations include identifying a second dominant vertex or a seconddominant side for the observation bounding shape. The operations includeperforming, for each of the one or more predicted shape locations, acomparison of the first dominant vertex or the first dominant side tothe second dominant vertex or the second dominant side. The operationsinclude determining at least one estimated current location of the firstdominant vertex or the first dominant side for the vehicle boundingshape based at least in part on the comparison.

Other aspects of the present disclosure are directed to various systems,apparatuses, non-transitory computer-readable media, user interfaces,and electronic devices.

These and other features, aspects, and advantages of various embodimentsof the present disclosure will become better understood with referenceto the following description and appended claims. The accompanyingdrawings, which are incorporated in and constitute a part of thisspecification, illustrate example embodiments of the present disclosureand, together with the description, serve to explain the relatedprinciples.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts a block diagram of an example autonomous vehicleaccording to example embodiments of the present disclosure.

FIG. 2 depicts a block diagram of an example perception system accordingto example embodiments of the present disclosure.

FIG. 3 depicts a block diagram of an example tracking system accordingto example embodiments of the present disclosure.

FIG. 4 depicts a block diagram of an example vehicle filter according toexample embodiments of the present disclosure.

FIG. 5 depicts a block diagram of an example computing system accordingto example embodiments of the present disclosure.

FIGS. 6A and 6B depicts a flow chart diagram of an example method totrack vehicles according to example embodiments of the presentdisclosure.

FIG. 7 depicts a graphical diagram of example predicted shape locationsaccording to example embodiments of the present disclosure.

FIG. 8 depicts a graphical diagram of an example sensor observationaccording to example embodiments of the present disclosure.

FIG. 9 depicts a graphical diagram of an example observation boundingshape according to example embodiments of the present disclosure.

FIG. 10 depicts a graphical diagram of an example technique foridentifying a dominant vertex according to example embodiments of thepresent disclosure.

FIG. 11 depicts a graphical diagram of an example technique foridentifying a dominant vertex according to example embodiments of thepresent disclosure.

FIG. 12 depicts a graphical diagram of an example technique foridentifying a dominant vertex according to example embodiments of thepresent disclosure.

FIG. 13 depicts a graphical diagram of an example technique forgenerating an occlusion map according to example embodiments of thepresent disclosure.

FIG. 14 depicts an example occlusion map according to exampleembodiments of the present disclosure.

FIG. 15 depicts a graphical diagram of example predicted shape locationsand an example shape location according to example embodiments of thepresent disclosure.

FIG. 16 depicts a graphical diagram of an example technique toinitialize a vehicle bounding shape according to example embodiments ofthe present disclosure.

FIG. 17 depicts a flow chart diagram of an example method to initializea vehicle bounding shape according to example embodiments of the presentdisclosure.

FIG. 18 depicts a flow chart diagram of an example method to detect poorestimates of vehicle location according to example embodiments of thepresent disclosure.

FIG. 19 depicts a graphical diagram of an example technique to detectpoor estimates of vehicle location according to example embodiments ofthe present disclosure.

FIG. 20 depicts a graphical diagram of an example technique to detectpoor estimates of vehicle location according to example embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Generally, the present disclosure is directed to systems and methods fortracking vehicles or other objects that are perceived by an autonomousvehicle. In particular, in some implementations of the presentdisclosure, an autonomous vehicle can include a tracking system thatimplements a vehicle filter to estimate a current location and/or otherparameters of each of one or more tracked vehicles based at least inpart on one or more sensor observations that correspond to the trackedvehicles. In some implementations, the vehicle filter can be or includean unscented Kalman filter. According to an aspect of the presentdisclosure, the vehicle filter can employ a motion model that models thelocation of the tracked vehicle using a vehicle bounding shape and anobservation model that infers the location of an observation boundingshape from sensor observations. Furthermore, according to another aspectof the present disclosure, a dominant vertex or side from eachrespective bounding shape can be identified and used to update orotherwise correct one or more predicted shape locations associated withthe vehicle bounding shape based on a shape location associated with theobservation bounding shape. By updating or otherwise correcting the oneor more predicted shape locations based on the shape location, thetracking system can more accurately estimate a current location and/orother parameters of each tracked vehicle. In addition, because in manyinstances the sensor observation and corresponding observation boundingshape correspond to only a portion of the vehicle, use of the dominantvertex and/or side when performing the updating or correction of the oneor more predicted shape locations results in more accurate vehiclelocation estimates relative to other techniques. As a result of suchimproved vehicle location tracking, the ability to motion plan orotherwise control the autonomous vehicle based on such tracked vehiclelocations is enhanced, thereby reducing vehicle collisions and furtherimproving passenger safety and vehicle efficiency.

More particularly, in some implementations, an autonomous vehicle caninclude a tracking system that tracks the locations of proximatevehicles or other objects based on sensor observations. The autonomousvehicle can be a ground-based autonomous vehicle (e.g., car, truck, bus,etc.), an air-based autonomous vehicle (e.g., airplane, drone,helicopter, or other aircraft), or other types of vehicles (e.g.,watercraft).

According to an aspect of the present disclosure, in someimplementations, the tracking system includes a vehicle filter thatemploys a motion model to predict one or more predicted shape locationsfor a vehicle bounding shape that is representative of a trackedvehicle. For example, the vehicle bounding shape can adhere to anappearance model that models the appearance of the tracked vehicle. Asone example, the vehicle bounding shape can be a bounding polygon suchas, for example, a bounding rectangle that has a length and width. Asanother example, the vehicle bounding shape can be a bounding polyhedronsuch as, for example, a bounding cube.

In some implementations, the motion model can predict the one or morepredicted shape locations for the vehicle bounding shape based at leastin part on a previous state of the tracked vehicle. As examples, theprevious state can include a previous location, size, orientation,velocity, acceleration, yaw rate, and/or other information for thetracked vehicle and/or the vehicle bounding shape that is representativeof the tracked vehicle. Thus, the vehicle filter can employ the motionmodel to iteratively predict one or more predicted shape locations forthe vehicle bounding shape that correspond to locations at which thevehicle is expected to be observed at the current iteration. In someimplementations, the motion model can simply predict one or morepredicted locations for the tracked vehicle and the one or morepredicted shape locations for the vehicle bounding shape can be derived(e.g., by the motion model, the observation model, or other trackingsystem components) from such one or more predicted locations for thetracked vehicle (e.g., through combination with a first size associatedwith the vehicle bounding shape).

In some implementations, the vehicle filter can also include and/oremploy an observation model to determine a shape location for anobservation bounding shape based at least in part on observation datadescriptive of one or more sensor observations. For example, theobservation model can fit the observation bounding shape around aplurality of observed points (e.g., points detected by a Light Detectionand Ranging (LIDAR) system), thereby providing a shape location for theobservation bounding shape. Similar to the vehicle bounding shape, theobservation bounding shape can be a bounding polygon (e.g., rectangle),bounding polyhedron (e.g., cube), or other bounding shapes (e.g.,vehicle type-specific shapes). Thus, the vehicle filter can employ theobservation model to iteratively determine a shape location for theobservation bounding shape that corresponds to a location at which thevehicle was at least partially observed at the current location.

According to another aspect of the present disclosure, the vehiclefilter or other component of the tracking system can determine at leastone estimated shape location for the vehicle bounding shape based atleast in part on a comparison of the one or more predicted shapelocations predicted by the motion model for the vehicle bounding shapeto the shape location of the observation bounding shape. For example, todetermine the at least one estimated shape location, the vehicle filtercan update, correct, or otherwise modify the one or more predicted shapelocations predicted by the motion model based on the shape location.

As one example, the vehicle filter can assign a weight to each of theone or more predicted shape locations predicted by the motion model,where the weight assigned to each predicted shape location correspondsto an amount of agreement between such predicted shape location and theshape location. In some implementations, a Gaussian representation ofthe estimated shape location or other estimated state parameters can becomputed from the weights.

More particularly, according to an aspect of the present disclosure, adominant vertex and/or side can be identified for each predicted shapelocation and, correspondingly, the shape location. For example, theobservation model of the vehicle filter can predict the location of thedominant vertex and/or side for each predicted shape location and/or forthe shape location.

As examples, the dominant vertex for each predicted shape location canbe, in some instances, a nearest unoccluded vertex. The dominant sidefor each predicted shape location can be, in some instances, a side thatincludes the dominant vertex and corresponds to a length of the vehicle.In some instances, the dominant vertex for the observation boundingshape can be the vertex that geometrically corresponds to the dominantvertex identified for at least one of the predicted shape locations. Insome instances, the dominant side for the observation bounding shape canbe the side that includes the dominant vertex and has an orientationthat is closest to an expected orientation (e.g., an expected heading ofthe tracked vehicle).

The locations of such dominant vertices and/or sides can be used todetermine the at least one estimated shape location. For example, thelocations of the dominant vertex and/or side for each predicted shapelocation predicted by the motion model can be respectively compared tothe locations of the dominant vertex and/or side of the observationbounding shape. The one or more predicted shape locations predicted bythe motion model can be updated or otherwise corrected based on suchcomparison. In such fashion, the predicted locations of the vehiclebounding shape can be updated or otherwise corrected based on thelocations of the dominant vertex and/or sides, rather than the footprintor centroid of the observation bounding shape as a whole. This isbeneficial because, in many instances, the sensor observations captureor otherwise correspond to only a portion of the tracked vehicle.Therefore, use of the dominant vertex and/or side of the observationbounding shape to update or otherwise correct the vehicle boundingshapes is more appropriate than use of the footprint or centroid of theobservation bounding shape.

As an example, in some implementations, the systems and methods of thepresent disclosure can identify the dominant vertex for each of the oneor more predicted shape locations predicted by the motion model byobtaining an occlusion map that describes occluded and unoccluded areas.In one example, the tracking system can generate the occlusion map byray casting from the autonomous vehicle to observed points. Such raycasting enables the tracking system to determine, for each observedpoint, areas that are occluded by the object that corresponds to suchobserved point. Areas that are not occluded by an object can bedesignated as unoccluded or vacant.

The vehicle filter can use the occlusion map to iteratively analyze, foreach of the one or more predicted shape locations, one or more nearestvertices in order of proximity until an unoccluded vertex is identified.In some implementations, when a nearest unoccluded vertex cannot beidentified for one of the predicted shape locations, the vehicle filtercan select a closest occluded vertex and increase a covariance valueassociated with the tracked vehicle. The dominant vertex of theobservation bounding shape can be identified in the same fashion or canbe identified by using geometry to identify the vertex of theobservation bounding shape that corresponds to a previously selecteddominant vertex of the vehicle bounding shape.

As another example, in some implementations, the systems and methods ofthe present disclosure can identify the dominant side for each of theone or more predicted shape locations predicted by the motion model byidentifying the side that corresponds to a closest unoccluded length ofthe tracked vehicle. For example, the dominant side can be the side thatincludes the dominant vertex and corresponds to a length of the vehicle.The dominant side of the observation bounding shape can be the side thatincludes the dominant vertex and is closest to an expected heading(e.g., a heading of one or more of the predicted shape locations or aprevious shape location). As described above, the locations of thedominant vertices and/or sides can be used to determine the at least oneestimated shape location (e.g., by correcting the predicted shapelocation(s)).

In addition, according to another aspect of the present disclosure, thevehicle bounding shape can have a size that is maintained iteration overiteration and updated intermittently based on the size of theobservation bounding shape. In particular, in some implementations, thevehicle bounding shape can have a first size defined by one or morefirst values respectively for one or more size parameters while theobservation bounding shape has a second size defined by one or moresecond values respectively for the one or more size parameters. Thesecond size of the observation bounding shape can be adjusteddynamically (e.g., iteration over iteration based on each newobservation). In one particular example, the vehicle bounding shape canbe a vehicle bounding rectangle, the observation bounding shape can bean observation bounding rectangle, and the one or more size parameterscan include a width and a length. In another example, the boundingshapes can be bounding boxes in three dimensions.

According to an aspect of the present disclosure, at a first instance inwhich a proximate vehicle is observed, the vehicle bounding shape can beinitialized to have minimum values for the one or more size parameters.Thereafter, the largest values for a given size parameter than have beenobserved can be used for the vehicle bounding shape moving forward.Thus, the size of the vehicle bounding shape can grow based on sensorobservations, but typically will not shrink. In such fashion, thevehicle bounding shape used to describe the state of the tracked vehiclewill avoid underestimating the size of the tracked vehicle, which can bebeneficial in instances in which at least a portion of the trackedvehicle is occluded.

As one example technique to implement the above described concept, ateach iteration in which an observation bounding shape is identified, thesecond value of the observation bounding shape for each size parametercan be compared to the first value of the vehicle bounding shape forsuch size parameter. In instances in which the second value is greaterthan the corresponding first value, the vehicle bounding shape can beupdated to have the second value for such size parameter. Thus, when theobservation bounding shape—which typically changes sizes at eachiteration to conform to the sensor observation—exceeds the size of thevehicle bounding shape for a given size parameter, the size of thevehicle bounding shape can be increased to match. However, in someimplementations, in order to be robust to segmentation failures andspurious observation data, a certain percentage of values (e.g., tenpercent) at the extremes for each size parameter can be rejected orother filtering or outlier detection techniques can be performed.

Carrying the size of the vehicle bounding shape forward from iterationto iteration allows the vehicle filter to accurately estimate the sizeand location of the tracked vehicle even when the corresponding sensorobservation for a given iteration does not reflect the true size of thetracked vehicle. As an example, in an example instance in which atracked vehicle has turned and/or become partially occluded and thecorresponding sensor observation now only corresponds to a smallerportion of the vehicle, the systems and methods of the presentdisclosure can still track the vehicle accurately because the size ofthe vehicle bounding shape is based on size data captured over a numberof previous iterations. Stated differently, even in instances in whichthe most recent sensor observations capture or otherwise correspond toonly a small portion of the tracked vehicle (e.g., the rear of thetracked vehicle), if the location of the dominant vertex and/or dominantside of the observation bounding shape is correctly captured andidentified, then such information can be used to update or otherwisecorrect the predicted shape location of the vehicle bounding shape,which retains the size values from previous iterations. Thus, since thelocation of the dominant vertex and/or dominant side of the observationbounding shape is used rather than the size, footprint, or centroid ofthe observation bounding shape, fluctuations in the size of theobservation bounding polygon (e.g., as the tracked vehicle turns, isoccluded, and/or is otherwise observed in less detail) do not negativelyinfluence the correction of the predicted shape locations of the vehiclebounding shape.

According to another aspect of the present disclosure, at a firstinstance in which a proximate vehicle is newly observed (e.g., a newsensor observation cannot be associated with an existing trackedvehicle), the systems and methods of the present disclosure caninitialize a new vehicle bounding shape for the new vehicle. As oneexample, initializing the new vehicle bounding shape can includeassigning respective minimum values to the vehicle bounding shape foreach of one or more size parameters (e.g., width and length). Forexample, the minimum values can correspond to the size of a smallvehicle.

In some implementations, initializing the new vehicle bounding shape forthe newly tracked vehicle can also include aligning the new vehiclebounding shape with a dominant vertex of a corresponding new observationbounding shape that has been fitted around the new sensor observation.In addition, initializing the new vehicle bounding shape for the newlytracked vehicle can include determining, based at least in part on anocclusion map, a first amount of unoccluded or vacant area associatedwith a first possible orientation of the new vehicle bounding shape anda second amount of unoccluded or vacant space associated with a secondpossible orientation of the new vehicle bounding shape. As it is morelikely that the vehicle extends into occluded space rather than vacantor unoccluded space without being observed, the orientation that has thesmaller amount of unoccluded or vacant space can be selected as theorientation for the new vehicle bounding shape. In instances in whichthere is no vacant space around the vehicle (e.g., a significantlyoccluded vehicle), the orientation can be selected based on the sizeparameter's minimum value to which the longest observed side is closest(e.g., is the longest observed side closest to a minimum length value ora minimum width value).

According to another aspect of the present disclosure, in someimplementations, the tracking system can also detect poor estimates oftracked vehicle state and, in response to detection of such a poorestimate, re-initialize or otherwise reset the vehicle filter for suchtracked vehicle. In one example, the tracking system can determine(e.g., using an occlusion map) a current amount of unoccluded or vacantarea associated with a current shape location of the vehicle boundingshape. When the current amount of unoccluded or vacant area associatedwith the current shape location of the vehicle bounding shape exceeds athreshold amount, the tracking system can re-initialize the vehiclebounding shape associated with the tracked vehicle (e.g., by performingthe initialization process described above). In another example, thetracking system can determine a current amount of overlap between acurrent shape location of the vehicle bounding shape and a current shapelocation. When the current amount of overlap between such predicted andshape locations is less than a threshold amount, the tracking system canre-initialize the vehicle bounding shape associated with the trackedvehicle. In such fashion, drifting of the filter state can be detectedand resolved.

Thus, the present disclosure provides systems and methods for trackingvehicles or other objects that are perceived by an autonomous vehicle.The improved ability to estimate the current location of such vehiclecan enable improved motion planning or other control of the autonomousvehicle, thereby reducing collisions and further enhancing passengersafety and vehicle efficiency. In addition, although the presentdisclosure discusses and makes reference to a vehicle filter thatassists in tracking proximate vehicles, aspects of the presentdisclosure can equally be applied to track objects other than vehicles.For example, bounding shapes that have the same or different appearancescan be used to predict and correct locations for other objects, inaddition or alternatively to vehicles.

As such, one technical effect and benefit of the present disclosure isimproved tracking of vehicles or other objects. In particular, thepresent disclosure provides techniques that enable a computing system toperform vehicle tracking with levels of accuracy and precision that wereheretofore unobtainable using existing computers and computingtechniques. Thus, the present disclosure improves the operation of anautonomous vehicle computing system and the autonomous vehicle itcontrols. In addition, the present disclosure provides a particularsolution to the problem of vehicle tracking and provides a particularway (e.g., identification of vertices and/or sides in bounding shapes)to achieve the desired outcome. The present disclosure also providesadditional technical effects and benefits, including, for example,enhancing passenger safety and improving vehicle efficiency by reducingcollisions.

Additional technical effects and benefits provided by aspects of thepresent disclosure include: better tracking of vehicles in instanceswhere sensor observations do not accurately depict the entirety of thevehicle shape or size; more stable tracking of vehicles; and improvedhandling of scenarios where portions of the tracked vehicle areoccluded.

With reference now to the Figures, example embodiments of the presentdisclosure will be discussed in further detail.

Example Devices and Systems

FIG. 1 depicts a block diagram of an example autonomous vehicle 10according to example embodiments of the present disclosure. Theautonomous vehicle 10 is capable of sensing its environment andnavigating without human input. The autonomous vehicle 10 can be aground-based autonomous vehicle (e.g., car, truck, bus, etc.), anair-based autonomous vehicle (e.g., airplane, drone, helicopter, orother aircraft), or other types of vehicles (e.g., watercraft).

The autonomous vehicle 10 includes one or more sensors 101, a vehiclecomputing system 102, and one or more vehicle controls 107. The vehiclecomputing system 102 can assist in controlling the autonomous vehicle10. In particular, the vehicle computing system 102 can receive sensordata from the one or more sensors 101, attempt to comprehend thesurrounding environment by performing various processing techniques ondata collected by the sensors 101, and generate an appropriate motionpath through such surrounding environment. The vehicle computing system102 can control the one or more vehicle controls 107 to operate theautonomous vehicle 10 according to the motion path.

The vehicle computing system 102 includes one or more processors 112 anda memory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storage media,such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flashmemory devices, etc., and combinations thereof.

The memory 114 can store information that can be accessed by the one ormore processors 112. For instance, the memory 114 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 116 that can be obtained, received, accessed, written,manipulated, created, and/or stored. In some implementations, thecomputing system 102 can obtain data from one or more memory device(s)that are remote from the system 102.

The memory 114 can also store computer-readable instructions 118 thatcan be executed by the one or more processors 112. The instructions 118can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 118 can be executed in logically and/or virtually separatethreads on processor(s) 112.

For example, the memory 114 can store instructions 118 that whenexecuted by the one or more processors 112 cause the one or moreprocessors 112 to perform any of the operations and/or functionsdescribed herein.

In some implementations, vehicle computing system 102 can furtherinclude a positioning system 122. The positioning system 122 candetermine a current position of the vehicle 10. The positioning system122 can be any device or circuitry for analyzing the position of thevehicle 10. For example, the positioning system 122 can determineposition by using one or more of inertial sensors, a satellitepositioning system, based on IP address, by using triangulation and/orproximity to network access points or other network components (e.g.,cellular towers, WiFi access points, etc.) and/or other suitabletechniques. The position of the vehicle 10 can be used by varioussystems of the vehicle computing system 102.

As illustrated in FIG. 1, the vehicle computing system 102 can include aperception system 103, a prediction system 104, and a motion planningsystem 105 that cooperate to perceive the surrounding environment of theautonomous vehicle 10 and determine a motion plan for controlling themotion of the autonomous vehicle 10 accordingly.

In particular, in some implementations, the perception system 103 canreceive sensor data from the one or more sensors 101 that are coupled toor otherwise included within the autonomous vehicle 10. As examples, theone or more sensors 101 can include a Light Detection and Ranging(LIDAR) system, a Radio Detection and Ranging (RADAR) system, one ormore cameras (e.g., visible spectrum cameras, infrared cameras, etc.),and/or other sensors. The sensor data can include information thatdescribes the location of objects within the surrounding environment ofthe autonomous vehicle 10.

As one example, for a LIDAR system, the sensor data can include thelocation (e.g., in three-dimensional space relative to the LIDAR system)of a number of points that correspond to objects that have reflected aranging laser. For example, a LIDAR system can measure distances bymeasuring the Time of Flight (TOF) that it takes a short laser pulse totravel from the sensor to an object and back, calculating the distancefrom the known speed of light.

As another example, for a RADAR system, the sensor data can include thelocation (e.g., in three-dimensional space relative to the RADAR system)of a number of points that correspond to objects that have reflected aranging radio wave. For example, radio waves (pulsed or continuous)transmitted by the RADAR system can reflect off an object and return toa receiver of the RADAR system, giving information about the object'slocation and speed. Thus, a RADAR system can provide useful informationabout the current speed of an object.

As yet another example, for one or more cameras, various processingtechniques (e.g., range imaging techniques such as, for example,structure from motion, structured light, stereo triangulation, and/orother techniques) can be performed to identify the location (e.g., inthree-dimensional space relative to the one or more cameras) of a numberof points that correspond to objects that are depicted in imagerycaptured by the one or more cameras. Other sensor systems can identifythe location of points that correspond to objects as well.

Thus, the one or more sensors 101 can be used to collect sensor datathat includes information that describes the location (e.g., inthree-dimensional space relative to the autonomous vehicle 10) of pointsthat correspond to objects within the surrounding environment of theautonomous vehicle 10.

In addition to the sensor data, the perception system 103 can retrieveor otherwise obtain map data 126 that provides detailed informationabout the surrounding environment of the autonomous vehicle 10. The mapdata 126 can provide information regarding: the identity and location ofdifferent travelways (e.g., roadways), road segments, buildings, orother items or objects (e.g., lampposts, crosswalks, curbing, etc.); thelocation and directions of traffic lanes (e.g., the location anddirection of a parking lane, a turning lane, a bicycle lane, or otherlanes within a particular roadway or other travelway); traffic controldata (e.g., the location and instructions of signage, traffic lights, orother traffic control devices); and/or any other map data that providesinformation that assists the computing system 102 in comprehending andperceiving its surrounding environment and its relationship thereto.

The perception system 103 can identify one or more objects that areperceived by the autonomous vehicle 10 based on sensor data receivedfrom the one or more sensors 101 and/or the map data 126. Thus, in someinstances, objects that are perceived by the autonomous vehicle 10 canbe objects that are proximate to the autonomous vehicle 10. Inparticular, in some implementations, the perception system 103 candetermine, for each object, state data that describes a current state ofsuch object. As examples, the state data for each object can describe anestimate of the object's: current location (also referred to asposition); current speed (also referred to as velocity); currentacceleration; current heading; current orientation; size/footprint(e.g., as represented by a bounding shape such as a bounding polygon orpolyhedron); class (e.g., vehicle versus pedestrian versus bicycleversus other); yaw rate; and/or other state information.

In some implementations, the perception system 103 can determine statedata for each object over a number of iterations. In particular, theperception system 103 can update the state data for each object at eachiteration. Thus, the perception system 103 can detect and track objects(e.g., vehicles) that are perceived by the autonomous vehicle 10 overtime.

The prediction system 104 can receive the state data from the perceptionsystem 103 and predict one or more future locations for each objectbased on such state data. For example, the prediction system 104 canpredict where each object will be located within the next 5 seconds, 10seconds, 20 seconds, etc. As one example, an object can be predicted toadhere to its current trajectory according to its current speed. Asanother example, other, more sophisticated prediction techniques ormodeling can be used.

The motion planning system 105 can determine a motion plan for theautonomous vehicle 10 based at least in part on the predicted one ormore future locations for the object and/or the state data for theobject provided by the perception system 103. Stated differently, giveninformation about the current locations of objects and/or predictedfuture locations of proximate objects, the motion planning system 105can determine a motion plan for the autonomous vehicle 10 that bestnavigates the autonomous vehicle 10 relative to the objects at suchlocations.

As one example, in some implementations, the motion planning system 105can evaluate a cost function for each of one or more candidate motionplans for the autonomous vehicle 10 based at least in part on thecurrent locations and/or predicted future locations of the objects. Forexample, the cost function can provide a cost (e.g., over time) ofadhering to a particular candidate motion plan. For example, the costprovided by a cost function can increase when the autonomous vehicle 10strikes another object and/or deviates from a preferred pathway (e.g., apreapproved pathway).

Thus, given information about the current locations and/or predictedfuture locations of objects, the motion planning system 105 candetermine a cost of adhering to a particular candidate pathway. Themotion planning system 105 can select or determine a motion plan for theautonomous vehicle 10 based at least in part on the cost function(s).For example, the motion plan that minimizes the cost function can beselected or otherwise determined. The motion planning system 105 canprovide the selected motion plan to a vehicle controller 106 thatcontrols one or more vehicle controls 107 (e.g., actuators or otherdevices that control gas flow, steering, braking, etc.) to execute theselected motion plan.

Each of the perception system 103, the prediction system 104, the motionplanning system 105, and the vehicle controller 106 can include computerlogic utilized to provide desired functionality. In someimplementations, each of the perception system 103, the predictionsystem 104, the motion planning system 105, and the vehicle controller106 can be implemented in hardware, firmware, and/or softwarecontrolling a general purpose processor. For example, in someimplementations, each of the perception system 103, the predictionsystem 104, the motion planning system 105, and the vehicle controller106 includes program files stored on a storage device, loaded into amemory and executed by one or more processors. In other implementations,each of the perception system 103, the prediction system 104, the motionplanning system 105, and the vehicle controller 106 includes one or moresets of computer-executable instructions that are stored in a tangiblecomputer-readable storage medium such as RAM hard disk or optical ormagnetic media.

FIG. 2 depicts a block diagram of an example perception system 103according to example embodiments of the present disclosure. The exampleperception system 103 includes a sensor data preprocessing system 204, asegmentation system 206, a tracking system 208, and a classificationsystem 210.

The sensor data preprocessing system 204 can receive sensor data fromthe one or more sensors 101 and can preprocess such sensor data. As anexample, in some implementations, the sensor data preprocessing system204 can retrieve and/or determine a plurality of locations respectivelyof a plurality of points observed by a LIDAR system, a RADAR system, oneor more cameras, and/or other sensor systems. In some implementations,the sensor data preprocessing system 204 can tag each point or group ofpoints with map information. For example, points or groups of points canbe tagged with labels that indicate that the point or groups of pointscorrespond to a sidewalk, a background, a road, a foreground, and/orother tags which can be based on map information. Additional informationsuch as height, distance, and/or region (e.g., road versus reportingzone versus segmentation zone) can be attached to each point.

In some implementations, the sensor data preprocessing system 204 canperform a background subtraction technique to preprocess the sensordata. For example, the sensor data preprocessing system 204 can removepoints that are within a certain threshold from a ground level, therebyreturning only points that correspond to foreground and backgroundobjects. In addition, in some implementations, the sensor datapreprocessing system 204 can remove points that correspond to backgroundobjects or other permanent obstacles or objects, thereby returning onlypoints that correspond to foreground objects that are proximate to theautonomous vehicle. The points that correspond to foreground objectsthat are proximate to the autonomous vehicle can be provided to thesegmentation system 206. In some implementations, the preprocessingsystem 204 can further include and implement a spurious point filterthat rejects points that do not correspond to true physical objects(e.g., noise).

According to another aspect of the present disclosure, the sensor datapreprocessing system 204 (or other system of the vehicle computingsystem) can include an occlusion map generator 212. The preprocessingsystem 204 can implement the occlusion map generator 212 to generate anocclusion map that describes occluded and unoccluded areas.

In one example, the sensor data preprocessing system 204 (or othersystem of the vehicle computing system) can generate the occlusion mapby casting rays from the autonomous vehicle to points that were observedby the one or more sensors 101. Such ray casting enables the sensor datapreprocessing system 204 to determine, for each observed point, areasthat are occluded by the object that corresponds to such observed point.Areas that are not occluded by an object can be designated as unoccludedor vacant.

To provide an example, FIG. 13 depicts a graphical diagram of an exampletechnique for generating an occlusion map according to exampleembodiments of the present disclosure. In particular, FIG. 13 depicts anautonomous vehicle 1302 that includes one or more sensor systems, whichare depicted at 1304. At least one of the sensor systems 1304 hasobserved a plurality of points, which are illustrated as circles in FIG.13. For example, the observed points include an observed point 1306. Inaddition, although the one or more sensor systems are depicted at 1304for ease of illustration and explanation, in some implementations,various sensor systems can be located at various different locationsother than the roof of the vehicle (e.g., a front bumper, tire well,etc.).

To generate the occlusion map, the sensor data preprocessing system 204(or other system of the vehicle computing system) can cast a ray fromthe sensor system 1304 that captured a point to the location of suchpoint. As an example, a ray 1308 can be cast from the sensor system 1304to the observed point 1306. Locations that are behind a point relativeto the sensor system 1304 can be designated as occluded while locationsthat are in-between a point and the sensor system 1304 can be designatedas unoccluded or vacant. By casting rays to each of the observed points,occluded and unoccluded areas can be determined. For example, anoccluded area 1310 is shown behind the illustrated observed points(including observed point 1306).

As another example, FIG. 14 depicts an example occlusion map accordingto example embodiments of the present disclosure. For the exampleocclusion map depicted in FIG. 14, an intensity of coloration can beindicative of occlusion height.

In some implementations, the occlusion map can be calculated in polarspace. In some implementations, the occlusion map can be generated in abackground thread as points are received. In some implementations, theocclusion map can be a 2.5 dimensional representation that identifies alowest and a highest point that is visible in each of a plurality ofcells (e.g., 10 cm by 10 cm cells). In some implementations, a groundsurface map can be used to calculate the minimum and/or maximum occludedheight for each cell.

Referring again to FIG. 2, the segmentation system 206 can receive thepreprocessed sensor data and can segment the data into one or moresensor observations that respectively correspond to one or more observedobjects. For example, each sensor observation can include one or morepoints grouped together and can correspond to a single physical object.The segmentation system 206 can provide the sensor observations to thetracking system 208. Thus, the segmentation system 206 can segment theplurality of points observed by the one or more sensors 101 into anumber of sensor observations that correspond to one or more observedobjects.

The tracking system 208 can tie together sensor observations over timeto estimate the state of each observed object. For example, the state ofeach object can include the object's current location (also referred toas position); current speed (also referred to as velocity); currentacceleration; current heading; current orientation; size/footprint(e.g., as represented by a bounding polygon); yaw rate; and/or otherstate information. In particular, the tracking system 208 can associatesensor observations to estimate the state of each object over time(e.g., as an iterative process that iteratively updates the estimatedstate of each object based on newly received sensor observations). Thetracking system 208 can provide the object state data to theclassification system 210. In addition, in some implementations, thetracking system 208 can further provide the object state data back tothe segmentation system 206 for use in identifying the sensorobservations at a subsequent time period.

The classification system 210 can predict a class for each object. Forexample, the classification system 210 can predict the class for eachobject based on the segmentation features of the sensor observations,tracking-based features (e.g., speed), and/or various other features. Insome implementations, the classification system 210 can classify eachobject into one of the four following example classes: vehicle,pedestrian, bicycle, other. In some implementations, the classificationsystem 210 can include a machine-learned classifier. In someimplementations, the classification system 210 can average a certainwindow (e.g., one second) of classifications to perform smoothing ofobject classification. The classification predicted by theclassification system 210 for each object can be added to the existingstate data for such object, and some or all of such state data can beprovided to the prediction system 104, which was discussed previouslywith reference to FIG. 1.

Each of the sensor data preprocessing system 204, the occlusion mapgenerator 212, the segmentation system 206, the tracking system 208, andthe classification system 210 can include computer logic utilized toprovide desired functionality. In some implementations, each of thesensor data preprocessing system 204, the occlusion map generator 212,the segmentation system 206, the tracking system 208, and theclassification system 210 can be implemented in hardware, firmware,and/or software controlling a general purpose processor. For example, insome implementations, each of the sensor data preprocessing system 204,the occlusion map generator 212, the segmentation system 206, thetracking system 208, and the classification system 210 includes programfiles stored on a storage device, loaded into a memory and executed byone or more processors. In other implementations, each of the sensordata preprocessing system 204, the occlusion map generator 212, thesegmentation system 206, the tracking system 208, and the classificationsystem 210 includes one or more sets of computer-executable instructionsthat are stored in a tangible computer-readable storage medium such asRAM hard disk or optical or magnetic media.

FIG. 3 depicts a block diagram of an example tracking system 208according to example embodiments of the present disclosure. The exampletracking system 208 includes an object associator 302, an object updater304, and an object birther/reaper 306.

The tracking system 208 can implement the object associator 302 toassociate new sensor observations to tracked objects. For example,relative distance from a new sensor observation to a tracked object,class of a tracked object versus class of the sensor observation, and/orother various features can be used to determine with which trackedobject to associate the new sensor observation.

The tracking system 208 can implement the object updater 304 to updatethe tracked objects based on the new sensor observations. Thus, as newsensor observations are collected and processed, the current state of atracked object can be updated based on such observation. Stateddifferently, as new evidence (e.g., a new sensor observation) of thecurrent state of a tracked object is obtained, the object updater 304can update the tracking system 208's best estimate of the current stateof such tracked object.

In some implementations, the object updater 304 can include a vehiclefilter 308. The tracking system 208 can implement the vehicle filter 308to estimate a current location and/or other related parameters (e.g.,size, orientation, etc.) of each of one or more tracked vehicles basedat least in part on one or more sensor observations that have beenrespectively associated with the tracked vehicles. In someimplementations, the vehicle filter 308 can be or include an unscentedKalman filter.

The tracking system 208 can implement the object birther/reaper 306 tobirth a new tracked object and/or reap previously tracked objects thatare no longer present. For example, the object birther/reaper 306 canbirth a new tracked object for a new sensor observation that was unableto be associated with a currently tracked object. For example, theobject birther/reaper 306 can reap a tracked object that the trackingsystem 208 expected to observe, but did not observe (e.g., did notobserve for a number of consecutive iterations). As another example, theobject birther/reaper 306 can reap a tracked object that is estimated tobe located in space that is unoccluded or vacant according to theocclusion map.

Each of the object associator 302, the object updater 304, the vehiclefilter 308, and the object birther/reaper 306 can include computer logicutilized to provide desired functionality. In some implementations, eachof the object associator 302, the object updater 304, the vehicle filter308, and the object birther/reaper 306 can be implemented in hardware,firmware, and/or software controlling a general purpose processor. Forexample, in some implementations, each of the object associator 302, theobject updater 304, the vehicle filter 308, and the objectbirther/reaper 306 includes program files stored on a storage device,loaded into a memory and executed by one or more processors. In otherimplementations, each of the object associator 302, the object updater304, the vehicle filter 308, and the object birther/reaper 306 includesone or more sets of computer-executable instructions that are stored ina tangible computer-readable storage medium such as RAM hard disk oroptical or magnetic media.

FIG. 4 depicts a block diagram of an example vehicle filter 308according to example embodiments of the present disclosure. The examplevehicle filter 308 includes a predictor 402 and a corrector 404.

The predictor 402 can be implemented to predict expected locations for atracked vehicle through use of a motion model 452. More particularly,according to an aspect of the present disclosure, the vehicle filter 308can employ a motion model 452 to model and predict the location of thetracked vehicle using a vehicle bounding shape. As an example, in someimplementations, one or more sigma points can be identified and thenused to predict shape locations of the vehicle bounding shape based on aprevious state of the tracked vehicle. The sigma points can encode meanand covariance information associated with the previous state of thetracked vehicle. As an example, in some implementations, a canonical setof sigma points can be generated for any number of dimensions n.

In addition, in some implementations, the motion model 452 can be aunicycle model. In particular, the unicycle model can assume fixedcurvature turns about the centroid of the vehicle bounding shape. Theunicycle model can further assume constant acceleration and yaw rate.

The corrector 404 can be implemented to correct the predicted locationsof the tracked vehicle based at least in part on one or more sensorobservations associated with such tracked vehicle. In particular, thecorrector 404 can employ an observation model 454 to generate anobservation bounding shape from sensor observations. In addition, insome implementations, the corrector 404 can compare each of thepredicted shape locations from the predictor 402 to a shape location ofthe observation bounding shape. In some implementations, the corrector404 can determine and apply a weight to each of the predicted shapelocations based on such comparison. For example, the respective weightassigned to each predicted shape location can be indicative of an amountof agreement between such predicted shape location and the shapelocation.

Furthermore, according to another aspect of the present disclosure, adominant vertex or side from each respective bounding shape can beidentified and used to update or otherwise correct one or more predictedshape locations associated with the vehicle bounding shape based on ashape location associated with the observation bounding shape. Byupdating or otherwise correcting the one or more predicted shapelocations based on the shape location, the vehicle filter 308 can moreaccurately estimate a current location and/or other state parameters ofeach tracked vehicle. In particular, because in many instances thesensor observation and corresponding observation bounding shapecorrespond to only a portion of the tracked vehicle, use of the dominantvertex and/or side by the corrector 404 when updating or correcting theone or more predicted shape locations results in more accurate vehiclelocation estimates relative to other techniques or shape features (e.g.,observation bounding shape centroid).

Each of the predictor 402 and the corrector 404 can include computerlogic utilized to provide desired functionality. In someimplementations, each of the predictor 402 and the corrector 404 can beimplemented in hardware, firmware, and/or software controlling a generalpurpose processor. For example, in some implementations, each of thepredictor 402 and the corrector 404 includes program files stored on astorage device, loaded into a memory and executed by one or moreprocessors. In other implementations, each of the predictor 402 and thecorrector 404 includes one or more sets of computer-executableinstructions that are stored in a tangible computer-readable storagemedium such as RAM hard disk or optical or magnetic media.

In some implementations, the motion model 452 and/or the observationmodel 454 included in the vehicle filter 308 can be or includemachine-learned models. As an example, FIG. 5 depicts a block diagram ofan example computing system 100 according to example embodiments of thepresent disclosure.

In particular, FIG. 5 illustrates an example implementation of thepresent disclosure in which the perception system 103 includes, employs,or otherwise leverages a machine-learned motion model 119 and/or amachine-learned observation model 120. The example system 100illustrated in FIG. 5 is provided as an example only. The vehiclecomputing systems of the present disclosure are not required to includeor use machine-learned models, but may if desired. Thus, components,systems, connections, and/or other aspects illustrated in FIG. 5 areoptional and are provided as examples of what is possible, but notrequired, to implement the present disclosure.

The example system 100 includes the vehicle computing system 102 and amachine learning computing system 130 that are communicatively coupledover a network 180.

The vehicle computing system 102 can store or include one or moremachine-learned models such as a machine-learned motion model 119 and amachine-learned observation model 120. For example, the machine-learnedmotion model 119 and/or the machine-learned observation model 120 can beor can otherwise include various machine-learned models such as, forexample, neural networks (e.g., deep neural networks), or othermulti-layer non-linear models. Neural networks can include recurrentneural networks (e.g., long short-term memory recurrent neuralnetworks), feed-forward neural networks, or other forms of neuralnetworks. As another example, the machine-learned motion model 119and/or the machine-learned observation model 120 can be or can otherwiseinclude one or more decision tree-based models, including, for example,boosted random forest models.

In some implementations, the computing system 102 can receive themachine-learned motion model 119 and/or the machine-learned observationmodel 120 from the machine learning computing system 130 over network180 and can store the machine-learned motion model 119 and/or themachine-learned observation model 120 in the memory 114. The computingsystem 102 can then use or otherwise implement the machine-learnedmotion model 119 and/or the machine-learned observation model 120 (e.g.,by processor(s) 112 to perform parallel object tracking across multipleinstances of proximate objects).

Thus, in some implementations, the prediction system 104 can employ themotion model 119 by inputting the previous state of the object into themachine-learned motion model 119 and receiving predicted locations ofthe tracked object (e.g., predicted bounding shape locations) as anoutput of the machine-learned motion model 119. Likewise, in someimplementations, the prediction system 104 can employ the observationmodel 120 by inputting the sensor observation into the machine-learnedobservation model 120 and receiving locations of the tracked object(e.g., a shape location of an observation bounding shape) as an outputof the machine-learned observation model 120.

The machine learning computing system 130 includes one or moreprocessors 132 and a memory 134. The one or more processors 132 can beany suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 134 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and combinations thereof.

The memory 134 can store information that can be accessed by the one ormore processors 132. For instance, the memory 134 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 136 that can be obtained, received, accessed, written,manipulated, created, and/or stored. In some implementations, themachine learning computing system 130 can obtain data from one or morememory device(s) that are remote from the system 130.

The memory 134 can also store computer-readable instructions 138 thatcan be executed by the one or more processors 132. The instructions 138can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 138 can be executed in logically and/or virtually separatethreads on processor(s) 132.

For example, the memory 134 can store instructions 138 that whenexecuted by the one or more processors 132 cause the one or moreprocessors 132 to perform any of the operations and/or functionsdescribed herein.

In some implementations, the machine learning computing system 130includes one or more server computing devices. If the machine learningcomputing system 130 includes multiple server computing devices, suchserver computing devices can operate according to various computingarchitectures, including, for example, sequential computingarchitectures, parallel computing architectures, or some combinationthereof.

In addition or alternatively to the machine-learned motion model 119and/or the machine-learned observation model 120 at the computing system102, the machine learning computing system 130 can include amachine-learned motion model 139 and/or a machine-learned observationmodel 140. For example, the models 139/140 can be or can otherwiseinclude various machine-learned models such as neural networks (e.g.,deep recurrent neural networks) or other multi-layer non-linear models.

In one example, the machine learning computing system 130 implements themachine-learned motion model 139 and/or the machine-learned observationmodel 140 for the vehicle computing system 102 according to aclient-server relationship. For example, the machine learning computingsystem 130 can implement the machine-learned motion model 139 and/or themachine-learned observation model 140 can be implemented as a portion ofa web service (e.g., an autonomous vehicle motion planning service).Thus, the machine-learned motion model 119 and/or the machine-learnedobservation model 120 can be stored and implemented at the vehiclecomputing system 102 and/or the machine-learned motion model 139 and/orthe machine-learned observation model 140 can be stored and implementedat the machine learning computing system 130.

In some implementations, the machine learning computing system 130and/or the computing system 102 can train the machine-learned models119, 120, 139, and/or 140 through use of a model trainer 160. The modeltrainer 160 can train the machine-learned models 119, 120, 139, and/or140 using one or more training or learning algorithms. One exampletraining technique is backwards propagation of errors. In someimplementations, the model trainer 160 can perform supervised trainingtechniques using a set of labeled training data. In otherimplementations, the model trainer 160 can perform unsupervised trainingtechniques using a set of unlabeled training data. The model trainer 160can perform a number of generalization techniques to improve thegeneralization capability of the models being trained. Generalizationtechniques include weight decays, dropouts, or other techniques.

In particular, the model trainer 160 can train one or more ofmachine-learned models 119, 120, 139, and/or 140 based on a set oftraining data 162. The training data 162 can include, for example,object state data that is labelled with one or more “correct” predictedlocations. As another example, the training data 162 can include sensorobservations that are labelled with “correct” locations. In someimplementations, the training data 162 is manually labelled. The modeltrainer 160 can be implemented in hardware, firmware, and/or softwarecontrolling one or more processors.

The computing system 102 can also include a network interface 124 usedto communicate with one or more systems or devices, including systems ordevices that are remotely located from the computing system 102. Thenetwork interface 124 can include any circuits, components, software,etc. for communicating with one or more networks (e.g., 180). In someimplementations, the network interface 124 can include, for example, oneor more of a communications controller, receiver, transceiver,transmitter, port, conductors, software and/or hardware forcommunicating data. Similarly, the machine learning computing system 130can include a network interface 164.

The network(s) 180 can be any type of network or combination of networksthat allows for communication between devices. In some embodiments, thenetwork(s) can include one or more of a local area network, wide areanetwork, the Internet, secure network, cellular network, mesh network,peer-to-peer communication link and/or some combination thereof and caninclude any number of wired or wireless links. Communication over thenetwork(s) 180 can be accomplished, for instance, via a networkinterface using any type of protocol, protection scheme, encoding,format, packaging, etc.

FIG. 5 illustrates one example computing system 100 that can be used toimplement the present disclosure. Other computing systems can be used aswell. For example, in some implementations, the computing system 102 caninclude the model trainer 160 and the training dataset 162. In suchimplementations, the machine-learned models 110 can be both trained andused locally at the computing system 102. As another example, in someimplementations, the computing system 102 is not connected to othercomputing systems.

In addition, components illustrated and/or discussed as being includedin one of the computing systems 102 or 130 can instead be included inanother of the computing systems 102 or 130. Such configurations can beimplemented without deviating from the scope of the present disclosure.The use of computer-based systems allows for a great variety of possibleconfigurations, combinations, and divisions of tasks and functionalitybetween and among components. Computer-implemented operations can beperformed on a single component or across multiple components.Computer-implemented tasks and/or operations can be performedsequentially or in parallel. Data and instructions can be stored in asingle memory device or across multiple memory devices.

Example Methods

FIGS. 6A and 6B depicts a flow chart diagram of an example method 600 totrack vehicles according to example embodiments of the presentdisclosure.

Referring first to FIG. 6A, at 602, a computing system obtains statedata that is descriptive of a previous state of a tracked vehicle thatis perceived by an autonomous vehicle. As examples, the previous statecan include a previous location, size, orientation, velocity,acceleration, yaw rate, and/or other information for the tracked vehicleand/or a vehicle bounding shape that is representative of the trackedvehicle.

At 604, the computing system predicts one or more predicted shapelocations for a vehicle bounding shape associated with the trackedvehicle. In some implementations, the computing system can employ amotion model to predict the one or more predicted shape locations forthe vehicle bounding shape based at least in part on the previous stateof the tracked vehicle.

As an example, in some implementations, one or more sigma points can beidentified and then used to predict shape locations of the vehiclebounding shape based on a previous state of the tracked vehicle. Thesigma points can encode mean and covariance information associated withthe previous state of the tracked vehicle. As an example, in someimplementations, a canonical set of sigma points can be generated in anynumber of dimensions n.

To provide a simplified example illustration, FIG. 7 depicts a graphicaldiagram of example predicted shape locations according to exampleembodiments of the present disclosure. In particular, FIG. 7 illustratesa past location 702 of a vehicle bounding shape that is associated witha previous state of the tracked vehicle. Thus, the location 702 of thevehicle bounding shape represents a past estimate of the location of thevehicle. Such previous state can further include information thatindicates that the vehicle was traveling according to a heading 712.

FIG. 7 further illustrates predicted shape locations 752, 754, and 756for the vehicle bounding shape. For example, such predicted shapelocations 752, 754, and 756 can have been predicted by a motion modelbased on the previous state data and three or more sigma points. Eachpredicted shape location 752, 754, and 756 has a respective heading 762,764, and 766 associated therewith.

Referring again to FIG. 6A, at 606, the computing system obtainsobservation data descriptive of one or more sensor observations. Moreparticularly, one or more sensors can be used to collect sensor datathat includes information that describes the location (e.g., inthree-dimensional space relative to the autonomous vehicle) of pointsthat correspond to objects within the surrounding environment of theautonomous vehicle. The observed points can be segmented into a numberof sensor observations. For example, each sensor observation can includeone or more observed points grouped together and can correspond to asingle physical object.

To provide a simplified example illustration, FIG. 8 depicts a graphicaldiagram of an example sensor observation 804 according to exampleembodiments of the present disclosure. The sensor observation 804 has aknown location relative to an autonomous vehicle 802. The sensorobservation 804 includes a number of observed points (e.g., points 806and 808) that have been grouped together. The sensor observation 804 isbelieved to correspond to a single object (e.g., vehicle) that isproximate to the autonomous vehicle 802.

Referring again to FIG. 6A, at 608, the computing system determines ashape location for an observation bounding shape associated with thetracked vehicle. For example, the computing system can employ anobservation model to fit the observation bounding shape around aplurality of observed points included in the sensor observation, therebyproviding a shape location for the observation bounding shape.

To provide a simplified example illustration, FIG. 9 depicts a graphicaldiagram of an example observation bounding shape according to exampleembodiments of the present disclosure. The observation bounding shapehas been fitted around the sensor observation 804 to identify a shapelocation 902 for the observation bounding shape. In particular, theobservation bounding shape has been fitted around the points (e.g.,points 806 and 808) included in the sensor observation 804 to identifythe shape location 902. As one example, the observation bounding shapecan be fitted around the plurality of observed points such thatobservation shape location 902 includes all of the plurality of observedpoints and minimizes a collective distance from the plurality of pointsto respective closest sides of the observation bounding shape.

Referring again to FIG. 6A, at 610, the computing system identifies afirst dominant vertex for each of the one or more predicted shapelocations predicted for the vehicle bounding shape. As an example, thedominant vertex for each predicted shape location can be a nearestunoccluded vertex.

More particularly, in some implementations, the computing system canidentify at 610 the dominant vertex for each of the one or morepredicted shape locations predicted by the motion model by obtaining anocclusion map that describes occluded and unoccluded areas. Thecomputing system can use the occlusion map to iteratively analyze, foreach of the one or more predicted shape locations, one or more nearestvertices in order of proximity until an unoccluded vertex is identified.In some implementations, when a nearest unoccluded vertex cannot beidentified for one of the predicted shape locations, the computingsystem can select a closest occluded vertex and increase a covariancevalue associated with the tracked vehicle.

To provide a simplified example illustration, FIG. 10 depicts agraphical diagram of an example technique for identifying a dominantvertex according to example embodiments of the present disclosure. Inparticular, FIG. 10 illustrates a predicted shape location 1004 of avehicle bounding shape relative to an autonomous vehicle 1002. Thevehicle bounding shape also has an associated heading 1006.

As illustrated in FIG. 10, a dominant vertex 1008 has been identifiedfor the predicted shape location 1004. In particular, the dominantvertex 1008 corresponds to a nearest unoccluded vertex.

To provide another simplified example illustration, FIG. 11 depicts agraphical diagram of an example technique for identifying a dominantvertex according to example embodiments of the present disclosure. Inparticular, FIG. 11 illustrates the predicted shape location 1004 of thevehicle bounding shape relative to the autonomous vehicle 1002.

However, in contrast to FIG. 10, as illustrated in FIG. 11, a sensorobservation 1110 indicates that an object is positioned between theautonomous vehicle 1002 and the predicted shape location 1004. Inparticular, the object represented by sensor observation 1110 occludesthe vertex 1008 of the predicted shape location 1004. As such, thevertex 1112 of the predicted shape location 1004 has been identified asthe dominant vertex. In particular, the dominant vertex 1112 is theclosest unoccluded vertex.

Referring again to FIG. 6A, at 612, the computing system identifies afirst dominant side for each of the one or more predicted shapelocations predicted for the vehicle bounding shape. As an example, insome implementations, the computing system can identify at 612 thedominant side for each of the one or more predicted shape locationspredicted by the motion model by identifying the side that correspondsto a closest unoccluded length of the tracked vehicle. For example, thedominant side can be the side that includes the dominant vertex andcorresponds to a length of the vehicle.

Referring now to FIG. 6B, at 614, the computing system identifies asecond dominant vertex for the observation bounding shape. As anexample, the second dominant vertex for the observation bounding shapecan be the vertex that geometrically corresponds to the dominant vertexidentified for at least one of the predicted shape locations.

More particularly, in some implementations, the dominant vertex of theobservation bounding shape can be identified in the same fashion asdescribed above with respect to 610. In other implementations, thedominant vertex of the observation bounding shape can be identified byusing geometry to identify the vertex of the observation bounding shapethat corresponds to a previously selected dominant vertex of the vehiclebounding shape.

To provide a simplified example illustration, FIG. 12 depicts agraphical diagram of an example technique for identifying a dominantvertex according to example embodiments of the present disclosure. Inparticular, FIG. 12 depicts the predicted shape location 1004. A firstdominant vertex 1112 has been identified for the predicted shapelocation 1004.

FIG. 12 further depicts a shape location 1214 of an observation boundingshape. A second dominant vertex 1216 has been identified for the shapelocation 1214. In particular, the second dominant vertex 1216geometrically corresponds to the first dominant vertex 1112 identifiedfor the predicted shape location 1004.

Referring again to FIG. 6B, at 616, the computing system identifies asecond dominant side for the observation bounding shape. As an example,the dominant side for the observation bounding shape can be the sidethat includes the dominant vertex and has an orientation that is closestto an expected orientation or heading (e.g., a heading of one or more ofthe predicted shape locations or a previous shape location).

At 618, the computing system performs, for each of the one or morepredicted shape locations, a comparison of the first dominant vertex andside of such predicted shape location to the second dominant vertex andside of the observation bounding shape. For example, a scoring formulacan compare the location of the first dominant vertex with the locationof the second dominant vertex. For example, a scoring formula cancompare the location and/or orientation of the first dominant side withthe location and/or orientation of the second dominant side.

To provide a simplified example illustration, FIG. 15 depicts agraphical diagram of example predicted shape locations and an exampleshape location according to example embodiments of the presentdisclosure. In particular, FIG. 15 illustrates the example predictedshape locations 752, 754, and 756 from FIG. 7 and the example shapelocation 902 from FIG. 9.

A first dominant vertex and side has been identified for each predictedshape location. In particular, a first dominant vertex 1502 and a firstdominant side 1503 have been identified for the predicted shape location752; a first dominant vertex 1504 and a first dominant side 1505 havebeen identified for the predicted shape location 754; and a firstdominant vertex 1506 and a first dominant side 1507 have been identifiedfor the predicted shape location 756. Likewise, a second dominant vertex1552 and a second dominant side 1553 have been identified for the shapelocation 902. As described above, the first dominant vertex and/or sidefor each of the shape locations 752, 754, and 756 can be compared to thesecond dominant vertex 1552 and the second dominant side 1553 of theshape location 902.

Referring again to FIG. 6B, at 620, the computing system determines atleast one estimated shape location for the vehicle bounding shape basedat least in part on the comparison performed at 618. For example, todetermine the at least one estimated shape location, the computingsystem can update, correct, or otherwise modify the one or morepredicted shape locations based on the comparisons to the shapelocation.

As one example, the vehicle filter can assign a weight to each of theone or more predicted shape locations predicted by the motion model,where the weight assigned to each predicted shape location correspondsto an amount of agreement between the locations of the first dominantvertex and/or side of such predicted shape location and the locations ofthe second dominant vertex and/or side of the shape location. In someimplementations, a Gaussian representation of the estimated shapelocation or other estimated state parameters can be computed from theweights.

Referring again to FIG. 15, based on the comparison, a higher weight canbe assigned to the predicted shape location 754, while a lower weightcan be assigned to the predicted shape locations 752 and 756. Thus,because the first dominant vertex 1504 and the first dominant side 1505of the predicted shape location 754 have a larger amount of agreementwith the second dominant vertex 1552 and the second dominant side 1553of the shape location 902 than do the vertices 1502 and 1506 and thesides 1503 and 1507, the predicted shape location 754 can receive ahigher weight than the predicted shape locations 752 and 756.

In such fashion, the predicted locations of the vehicle bounding shapecan be updated or otherwise corrected based on the locations of thedominant vertex and/or sides, rather than the footprint or centroid ofthe observation bounding shape as a whole. This is beneficial because,in many instances, the sensor observations capture or otherwisecorrespond to only a portion of the tracked vehicle. Therefore, use ofthe dominant vertex and/or side of the observation bounding shape toupdate or otherwise correct the vehicle bounding shapes is moreappropriate than use of the footprint or centroid of the observationbounding shape.

According to another aspect of the present disclosure, at a firstinstance in which a proximate vehicle is newly observed (e.g., a newsensor observation cannot be associated with an existing trackedvehicle), the systems and methods of the present disclosure caninitialize a new vehicle bounding shape for the new vehicle. As oneexample, FIG. 17 depicts a flow chart diagram of an example method 1700to initialize a vehicle bounding shape according to example embodimentsof the present disclosure.

At 1702, a computing system receives a new sensor observation that isunassociated with a currently tracked vehicle. At 1704, the computingsystem determines a new observation bounding shape for the new sensorobservation.

At 1706, the computing system generates a new vehicle bounding shape andassigns a minimum size to the new vehicle bounding shape. For example,the minimum size can include one or more minimum values that correspondto the size of a small vehicle.

At 1708, the computing system aligns a dominant vertex of the vehiclebounding shape with a dominant vertex of the observation bounding shape.

At 1710, the computing system determines a first amount of unoccluded orvacant area associated with a first possible orientation of the vehiclebounding shape.

At 1712, the computing system determines a second amount of unoccludedor vacant area associated with a second possible orientation of thevehicle bounding shape.

At 1714, the computing system selects the possible orientation that hasthe smaller amount of unoccluded or vacant area associated therewith.

To provide a simplified example illustration, FIG. 16 depicts agraphical diagram of an example technique to initialize a vehiclebounding shape according to example embodiments of the presentdisclosure.

In particular, FIG. 16 illustrates a new sensor observation 1602. A newobservation bounding shape 1604 has been fitted onto the sensorobservation 1602.

A new vehicle bounding shape has been initialized for the new sensorobservation 1602 and has been aligned with a dominant vertex of theobservation bounding shape 1604.

Two possible orientations exist for the vehicle bounding shape: a firstorientation 1606 and a second possible orientation 1608. As describedabove, an amount of unoccluded or vacant area can be determined for eachpossible orientation 1606 and 1608.

As illustrated in FIG. 16, a significant portion of the second possibleorientation 1608 of the vehicle bounding shape extends into unoccludedarea (shown generally at 1610). By contrast, nearly all of the firstpossible orientation 1608 of the vehicle bounding shape is within anoccluded area 1612.

Therefore, the second possible orientation 1608 includes a larger amountof unoccluded or vacant area than the first possible orientation 1606includes. As such, the first possible orientation 1606 can be selectedfor the vehicle bounding shape and the vehicle bounding shape can beinitialized with the first possible orientation 1606.

According to another aspect of the present disclosure, in someimplementations, the systems and methods of the present disclosure canalso detect poor estimates of tracked vehicle state and, in response todetection of such a poor estimate, re-initialize or otherwise reset thevehicle filter for such tracked vehicle. As one example, FIG. 18 depictsa flow chart diagram of an example method 1800 to detect poor estimatesof vehicle location according to example embodiments of the presentdisclosure.

At 1802, a computing system determines a first amount of unoccluded orvacant area associated with a current estimated shape location of thevehicle bounding shape.

At 1804, the computing system determines whether the first amountexceeds a first threshold value. If it is determined at 1804 that thefirst amount exceeds the first threshold value, then method 1800proceeds to 1812 and re-initializes the vehicle bounding shape. However,if it is determined at 1804 that the first amount does not exceed thefirst threshold value, then method 1800 proceeds to 1806.

To provide one simplified example illustration, FIG. 19 depicts agraphical diagram of an example technique to detect poor estimates ofvehicle location according to example embodiments of the presentdisclosure. In particular, FIG. 19 illustrates a current estimated shapelocation 1902 of a vehicle bounding shape. The current estimated shapelocation 1902 includes both unoccluded area 1904 and occluded area 1906.If the current estimated shape location 1902 includes too muchunoccluded area 1904 (e.g., greater than a first threshold value), thenit can be detected as a poor estimate.

Referring again to FIG. 18, at 1806, the computing system determines asecond amount of overlap between the current estimated shape locationand a current shape location.

At 1808, the computing system determines whether the second amount isless than a second threshold value. If it is determined at 1808 that thesecond amount is less than the second threshold value, then method 1800proceeds to 1812. However, if it is determined at 1808 that the secondamount is not less than the second threshold value, then method 1800proceeds to 1810.

To provide one simplified example illustration, FIG. 20 depicts agraphical diagram of an example technique to detect poor estimates ofvehicle location according to example embodiments of the presentdisclosure. In particular, FIG. 20 illustrates a current estimated shapelocation 2002 of a vehicle bounding shape and a location 2004 of anobservation bounding shape. The shapes 2002 and 2004 have an overlappingarea shown generally at 2006. If the amount of overlapping area 2006becomes too small (e.g., less than the second threshold value) then thecurrent estimate shape location 2002 can be detected as a poor estimate.

Referring again to FIG. 18, at 1810, the computing system proceeds tothe next iteration with the current estimated shape location.

However, at 1812, the computing system re-initializes the vehiclebounding shape. For example, the computing system can perform some orall of method 1700 of FIG. 17 to initialize the vehicle bounding shape.

Additional Disclosure

The technology discussed herein makes reference to servers, databases,software applications, and other computer-based systems, as well asactions taken and information sent to and from such systems. Theinherent flexibility of computer-based systems allows for a greatvariety of possible configurations, combinations, and divisions of tasksand functionality between and among components. For instance, processesdiscussed herein can be implemented using a single device or componentor multiple devices or components working in combination. Databases andapplications can be implemented on a single system or distributed acrossmultiple systems. Distributed components can operate sequentially or inparallel.

While the present subject matter has been described in detail withrespect to various specific example embodiments thereof, each example isprovided by way of explanation, not limitation of the disclosure. Thoseskilled in the art, upon attaining an understanding of the foregoing,can readily produce alterations to, variations of, and equivalents tosuch embodiments. Accordingly, the subject disclosure does not precludeinclusion of such modifications, variations and/or additions to thepresent subject matter as would be readily apparent to one of ordinaryskill in the art. For instance, features illustrated or described aspart of one embodiment can be used with another embodiment to yield astill further embodiment. Thus, it is intended that the presentdisclosure cover such alterations, variations, and equivalents.

In particular, although FIGS. 6A-B, 17, and 18 respectively depict stepsperformed in a particular order for purposes of illustration anddiscussion, the methods of the present disclosure are not limited to theparticularly illustrated order or arrangement. The various steps of themethods 600, 1700, and 1800 can be omitted, rearranged, combined, and/oradapted in various ways without deviating from the scope of the presentdisclosure.

1.-20. (canceled)
 21. A computer-implemented method for tracking vehicles that are perceived by an autonomous vehicle, the method comprising: obtaining, by one or more computing devices included in the autonomous vehicle, state data descriptive of a previous state of a tracked vehicle that is perceived by the autonomous vehicle, wherein the previous state of the tracked vehicle comprises at least one previous location of the tracked vehicle or a vehicle bounding shape associated with the tracked vehicle; predicting, by the one or more computing devices, one or more first locations for a first portion of the vehicle bounding shape based at least in part on the state data; obtaining, by the one or more computing devices, observation data descriptive of one or more sensor observations; determining, by the one or more computing devices, a second location of a second portion of an observation bounding shape associated with the tracked vehicle based at least in part on the observation data; and determining, by the one or more computing devices, at least one estimated current location of the first portion of the vehicle bounding shape associated with the tracked vehicle based at least in part on a comparison of the one or more first locations for the first portion of the vehicle bounding shape to the second location of the second portion of the observation bounding shape.
 22. The computer-implemented method of claim 21, wherein determining, by the one or more computing devices, the at least one estimated current location of the first portion of the vehicle bounding shape associated with the tracked vehicle comprises assigning, by the one or more computing devices, a respective weight to each of the one or more first locations based at least in part on the comparison of the one or more first locations for the vehicle bounding shape to the second location of the observation bounding shape, the respective weight assigned to each first location indicative of an amount of agreement between such first location and the second location of the observation bounding shape.
 23. The computer-implemented method of claim 21, wherein predicting, by the one or more computing devices, the one or more first locations for the first portion of the vehicle bounding shape comprises: predicting, by the one or more computing devices, one or more first shape locations for the vehicle bounding shape; and respectively identifying, by the one or more computing devices, a nearest unoccluded vertex of each of the one or more first shape locations.
 24. The computer-implemented method of claim 23, wherein predicting, by the one or more computing devices, the one or more first locations for the first portion of the vehicle bounding shape further comprises, for each of the one or more first shape locations: identifying, by the one or more computing devices, a dominant side that includes the nearest unoccluded vertex and corresponds to a length of the tracked vehicle.
 25. The computer-implemented method of claim 23, wherein respectively identifying, by the one or more computing devices, the nearest unoccluded vertex of each of the one or more first shape locations comprises: obtaining, by the one or more computing devices, an occlusion map that describes occluded and unoccluded areas; and for each of the one or more first shape locations, iteratively analyzing, by the one or more computing devices, in order of proximity to the autonomous vehicle, one or more nearest vertices of such bounding shape relative to the occlusion map until an unoccluded vertex is identified.
 26. The computer-implemented method of claim 23, wherein the method further comprises: when the respective nearest unoccluded vertex cannot be identified for one of the first shape locations: selecting, by the one or more computing devices, a nearest occluded vertex; and increasing, by the one or more computing devices, a covariance value associated with the vehicle bounding shape.
 27. The computer-implemented method of claim 21, wherein: obtaining, by the one or more computing devices, the observation data descriptive of the one or more sensor observations comprises obtaining, by the one or more computing devices, the observation data descriptive of a plurality of observed points; and determining, by the one or more computing devices, the second location of the second portion of the observation bounding shape comprises fitting, by the one or more computing devices, the observation bounding shape around the plurality of observed points such that the fitted observation bounding shape includes all of the plurality of observed points and minimizes a collective distance from the plurality of points to respective closest sides of the observation bounding shape.
 28. The computer-implemented method of claim 21, wherein determining, by the one or more computing devices, the second location of the second portion of the observation bounding shape associated with the tracked vehicle comprises: identifying, by the one or more computing devices, the second vertex that corresponds to the first vertex of the vehicle bounding polygon; and selecting, by the one or more computing devices, the second side for the observation bounding shape that includes the second vertex and is closest in orientation to a predicted heading associated with at least one of the one or more first shape locations.
 29. The computer-implemented method of claim 21, wherein: the vehicle bounding shape has a first size defined by one or more first values respectively for one or more size parameters; the observation bounding shape has a second size defined by one or more second values respectively for the one or more size parameters; and the method further comprises: comparing, by the one or more computing devices, the second value for each size parameter to the first value for such size parameter; and for at least one size parameter for which the second value is greater than the first value, updating, by the one or more computing devices, the first value for such size parameter to equal the second value.
 30. The computer-implemented method of claim 29, wherein the vehicle bounding shape comprises a vehicle bounding rectangle, the observation bounding shape comprises an observation bounding rectangle, and the one or more size parameters comprise a width and a length.
 31. The computer-implemented method of claim 21, wherein the first portion of the vehicle bounding shape comprises a first vertex or a first side of the vehicle bounding shape.
 32. The computer-implemented method of claim 31, wherein the second portion of the observation bounding shape comprises a second vertex or a second side of the observation bounding shape.
 33. A computer system, comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computer system to perform operations, the operations comprising: predicting, by the one or more computing devices, one or more first shape locations for a vehicle bounding shape associated with a tracked vehicle that is perceived by an autonomous vehicle; identifying, by the one or more computing devices, a first dominant portion for each of the one or more first shape locations predicted for the vehicle bounding shape; obtaining, by the one or more computing devices, observation data descriptive of one or more sensor observations; determining, by the one or more computing devices, a second shape location for an observation bounding shape associated with the tracked vehicle based at least in part on the observation data; identifying, by the one or more computing devices, a second dominant portion for the observation bounding shape; and determining, by the one or more computing devices, at least one estimated shape location for the vehicle bounding shape based at least in part on a comparison of a first location of the first dominant portion for each of the one or more first shape locations to a second location of the second dominant portion for the observation bounding shape.
 34. The computer system of claim 33, wherein determining, by the one or more computing devices, the at least one estimated shape location for the vehicle bounding shape based at least in part on the comparison of the first location of the first dominant portion for each of the one or more first shape locations to the second location of the second dominant portion for the observation bounding shape comprises assigning, by the one or more computing devices, a respective weight to each of the one or more first shape locations based at least in part on the comparison, the respective weight assigned to each first shape location indicative of an amount of agreement between the first location of the first dominant portion identified for such first shape location and the second location of the second dominant portion for the observation bounding shape.
 35. The computer system of claim 33, wherein identifying, by the one or more computing devices, the first dominant portion for each of the one or more first shape locations predicted for the vehicle bounding shape comprises identifying, by the one or more computing devices, a nearest unoccluded vertex for each of the one or more first shape locations.
 36. The computer system of claim 33, wherein the operations further comprise: identifying, by the one or more computing devices, a first dominant side for each of the one or more first shape locations predicted for the vehicle bounding shape; and identifying, by the one or more computing devices, a second dominant side for the observation bounding shape; wherein determining, by the one or more computing devices, the at least one estimated shape location for the vehicle bounding shape based at least in part on the comparison comprises determining, by the one or more computing devices, the at least one estimated shape location for the vehicle bounding shape based at least in part on the comparison and further based at least in part on a second comparison of a first orientation of the first dominant side for each of the one or more first shape locations to a second orientation of the second dominant side for the observation bounding shape.
 37. The computer system of claim 36, wherein identifying, by the one or more computing devices, the second dominant side for the observation bounding shape comprises identifying, by the one or more computing devices, the second dominant side that includes the second dominant portion and has the second orientation that is closest to an expected heading.
 38. An autonomous vehicle, comprising: a tracking system configured to track vehicles perceived by the autonomous vehicle, wherein the tracking system comprises: a motion model configured to predict one or more first shape locations for a vehicle bounding shape associated with a tracked vehicle; and an observation model configured to provide a second shape location of an observation bounding shape associated with the tracked vehicle based at least in part on observation data derived from one or more sensors; one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: identifying a first dominant portion for each of the one or more first shape locations predicted for the vehicle bounding shape; identifying a second dominant portion for the observation bounding shape; performing, for each of the one or more first shape locations, a comparison of the first dominant portion to the second dominant portion; and determining at least one estimated current location of the first dominant portion for the vehicle bounding shape based at least in part on the comparison.
 39. The autonomous vehicle of claim 38, wherein: the motion model comprises a machine-learned motion model; and the observation model comprises a machine-learned observation model.
 40. The autonomous vehicle of claim 38, wherein at least one of the motion model and the observation model comprises a boosted random forest model. 